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ClimateObservations SeriesEditor PaulD.Williams
PeterDomonkos
Elsevier
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CHAPTER1Landsurfaceobservations......................................1
1.1 Globalsystemofweatherandclimateobservations.....................1
1.2 Siteselectionandinstallationofinstruments................................5
1.3 Manualandautomatedobservations.............................................6
1.4 Temperature....................................................................................9
1.5 Humidity.......................................................................................11
1.6 Precipitation..................................................................................14
1.7 Winddirectionandwindspeed...................................................18
1.8 Atmosphericpressure...................................................................20
1.9 Sunshinedurationandradiation..................................................22
1.10 Cloudiness....................................................................................24
1.11 Otherclimatevariables................................................................25
1.12 Calibrationofinstrumentsandmaintenance...............................28 References....................................................................................30
CHAPTER2Upperairobservationandremotesensing............31
2.1 Upperairobservations:Climaticcharacteristics andtoolsfortheirobservation.....................................................31
2.2 RadiosondesI.Technologyandperformance ofobservations.............................................................................34
2.3 RadiosondesII.Spatialandtemporaldensity ofobservations.............................................................................39
2.4 Remotesensing............................................................................42
2.5 Weatherradars.............................................................................44
2.6 Satellitesintheobservationofweatherandclimate..................47
2.7 Space-basedobservations.............................................................49
2.8 Otherupperairobservations........................................................53
2.9 ClosingnotestoChapter1andthischapter................................55 References....................................................................................56
CHAPTER3Dataqualitycontrolanddatasetdevelopment........59
3.1 Errorsources................................................................................59
3.2 Kindsandindicationsofdataerrors............................................61
3.3 Phasesofqualitycontrol..............................................................66
3.4 Eliminationofdataerrors............................................................69
3.5 Qualitycontrolofextremevalues...............................................69
3.6 Datarescueanddigitation...........................................................70
3.7 Datagapsandgapfilling.............................................................72
3.8 Datagridding................................................................................76
3.9 Datasetdevelopment....................................................................77 References....................................................................................79
CHAPTER4Homogenizationtaskanditsprincipal approaches.........................................................83
4.1 Timeserieshomogenizationinthesystemof scientificfields.............................................................................83
4.2 Basicconceptsoftimeserieshomogenization............................84
4.3 Kindsofinhomogeneities............................................................86
4.4 Kindsofhomogenizationtasks....................................................89
4.5 Spatialrepresentativenessofhomogenized climaticdata.................................................................................90
4.6 Relationwithgeneralqualitycontrol..........................................92
4.7 Useofdocumentedinformation(metadata)................................95
4.8 Homogeneitytest.........................................................................99
4.9 Homogenizationwithoutneighborseries....................................99 References..................................................................................102
CHAPTER5Relativehomogenization:Thebasis....................105 5.1 Conceptofrelativehomogenization..........................................105
5.2 Traditionalapproach..................................................................106
5.3 Revolutionofmethodologyfromthe1990s..............................110
5.4 Timeseriescomparison.............................................................112
5.5 Detectionoftrendinhomogeneities...........................................118
5.6 Detectionofmultiplebreakpoints............................................120
5.7 Correctionofinhomogeneities...................................................123 References..................................................................................128
CHAPTER6Relativehomogenization:Optionaltools..............131 6.1 Multistepprocedures..................................................................131
6.2 Iteration......................................................................................132
6.3 Parameterization.........................................................................140
6.4 Relativetimeseriesofdailyresolution.....................................142
6.5 Ensemblehomogenization.........................................................144
6.6 Transformationofprobabilitydistribution................................145
6.7 Infillingdatagapswithinhomogenizationprocedures.............147
6.8 Pairwisedetectioninautomatichomogenization......................150
6.9 Multivariatedetection................................................................151
6.10 Combinationofhomogenizationmethods.................................153 References..................................................................................155
CHAPTER7Relativehomogenization:Specialproblems........159 7.1 Signal-to-noiseratio...................................................................159
7.2 Systematicbiasforregionalmeans...........................................160
7.3 Autocorrelation...........................................................................161
7.4 Cyclicalcomponents..................................................................166
7.5 Thresholddistanceforspatialcomparisons..............................173
7.6 Synchronousandsemi-synchronousinhomogeneities..............174
7.7 Short-terminhomogeneities.......................................................177
7.8 Weatherdependentinhomogeneities.........................................180
7.9 Homogenizationofprobabilitydistribution..............................182
7.10 Temporalresolutionofhomogenizationresults........................186
7.11 Wideapplicabilityofadditiveinhomogeneitymodel...............188 References..................................................................................189
CHAPTER8Aselectionofstatisticalhomogenization methods............................................................191
8.1 Methodsusingaccumulatedanomalies.....................................191
8.2 SNHT(StandardNormalHomogeneityTest)...........................195
8.3 RHtests(RelativeHomogenizationTests).................................197
8.4 MASH(MultipleAnalysisofSeries forHomogenization)..................................................................198
8.5 PHA(PairwiseHomogenizationAlgorithm).............................201
8.6 Climatol......................................................................................202
8.7 PRODIGE...................................................................................205
8.8 HOMER(HOMogenizationsoftwarEinR)..............................206
8.9 ACMANT(AppliedCaussinus-MestreAlgorithmfor homogenizingNetworksofclimaticTimeseries)....................211
8.10 Homogenizationmethodsforparticularclimaticelements......215 References..................................................................................216
CHAPTER9Accuracyofhomogenizationresults....................219 9.1 Conceptsofbenchmarking........................................................219
9.2 Constructionofbenchmarkdatasets..........................................221
9.3 Efficiencymeasures...................................................................225
9.4 Limitationsofthereliabilityoftestresults...............................228
9.5 Testsforbreakdetectionmethods.............................................230
9.6 HOMEbenchmarkexperiments................................................233
Abouttheauthors PeterDomonkos isaHungarianclimatologistlivinginSpainsince2009.Heisan expertonstatisticalclimatology,analysisofextremeclimaticevents,dataquality control,andtimeserieshomogenization.HeisamemberoftheHungarianMeteorologicalSocietyandsecretaryofESPERE(EnvironmentalScienceforEverybody RoundtheEarth).Between2009and2015,Dr.Domonkoswasaresearcheratthe UniversityRoviraiVirgili(Tarragona,Spain)andhasbeenafreeresearchersince then.Hehasdevelopedanautomatichomogenizationmethod(ACMANT),which wasfoundtobeoneofthemostaccuratemethodsbyvariousinternationaltestexperiments.Between2013and2015,heledfourinternationaltrainingsontimeseries homogenization,sponsoredbytheWorldMeteorologicalOrganization(WMO). Hehas104printedscientificpublicationstohisname.
Ro ´ bertTo ´ th isanexperiencedmeteorologistwithademonstratedhistoryofworkingintheenvironmentalservicesindustry.Skilledinmeteorologicalmeasurements, internationalagreementsonairqualityprotection,sustainabledevelopment,environmentalcompliance,andemergencymanagement,heisastrongresearchprofessionalwithamaster’sdegreeofpublicadministrationfromtheUniversityof Economy,Budapest,Hungary.HehasbeenheadoftheUnitforDataQualityControl attheHungarianMeteorologicalServicesince2020.HeisresponsiblefortheconventionalprecipitationmonitoringnetworkandisdeputyeditorinChiefof Legkor (thequarterlyjournaloftheHungarianMeteorologicalServiceandHungarianMeteorologicalSociety).In2008–09,hewaspresidentofUNEPMontrealProtocol Bureau.Hehasgivenlecturesonmeteorologicalobservationandinstrumentsat E€ otv € osLora ´ ndUniversity,Budapest,inthe1990s.
La ´ szlo ´ Nyitrai isacertifiedmeteorologist.Hegraduatedinmeteorologyfrom EotvosLora ´ ndUniversityinBudapest,Hungary,in1985.Hehasworkedforthe HungarianMeteorologicalServiceinthefieldofmeteorologicalmeasurements,data archiving,andclimatetables.Inaddition,hehasdealtwithaspectsofionospheric physicsaffectingradiowavepropagation,collectedrecordsofionosphericconditionsforshortwaveradiosignaltransport,andcorrespondedinternationallyonthis topic.Hehasinvestigatedglobaltrends,completenessanddeficienciesofmeteorologicaltroposphericandstratosphericupperairmeasurementsandtheirpresumed economicbackgroundinrelationtotheWMOmembercountries.Hehasattempted tocalculateatmosphericmoisturetransportfromradiosondeobservationsandpresentedhisstudiesatinternationalconferences.
Introduction Weneedobservedweatherandclimatedatatoknowhowtoprepareforoutdoor activities.Weatherforecastersneedmuchmoreobserveddatatomakereliable weatherpredictions.Andclimatescienceneedsevenmoreobserveddata,thatare denseinspaceandtime,tounderstandtherulesofweatherandclimateprocesses, toproducereliableclimatepredictions,andprovideprofessionalsupporttoapplied climatologicalresearch.Ofcourse,notonlytheamountofobserveddata,butalso theiraccuracyisimportant.Inaddition,observedclimaticdataareexpectedtobe fairlycomparablebothspatiallyandtemporally,astheyareoftenusedjointly,or toanalyzespatialandtemporalclimaticgradients.Thetemporalcomparabilityneeds alonganduninterruptedseriesofclimateobservationsperformedatthesamesites, inthesameenvironment,andfollowingfixedobservationrules.Ontheotherhand, fairspatialcomparabilityneedsnetworksofadequatelydenseanduniformly equippedobservingsites,andtheapplicationofcommonobservingrules.
Inthehistoryofinstrumentalclimateobservations,thedevelopmentofaccuracy, spatialdensity,andtheuniformityofobservingrulesaregradual.Togetherwiththe increasingspatialdensityofobservingstations,statisticalmethodshavebeendevelopedtooptimizetheinformationprovidedbythedataforclimatechangeandclimate variabilityestimations.Thesestatisticalmethodshavetwomaingroups:dataquality controlandtimeserieshomogenization.InsomeEuropeancities,timeseriesof instrumentaltemperatureandprecipitationobservationshavebeenrecordedfrom asearlyasthe18thcentury,buttheearliestrecordsarerarelyusedfortemporalgaps oftheobservationsandotherdataqualityproblems.Fromthesecondhalfofthe19th century,nationalmeteorologicalinstituteswereestablishedinmanycountries,and theyprovidespatiallycoordinatedclimateobservationsofhigh-levelprofessional standards.In1950,thecreationoftheWorldMeteorologicalOrganization (WMO)withintheUnitedNationsbroughtnewpossibilitiestotheworldwideregularizationofclimateobservationsandclimatedatamanagement.WithintheWMO, theWorldClimateDataMonitoringProgram(WCDMP)waslaunchedinthe1980s, whichreleased86documentsforimprovingtheuniformityandaccuracyofclimate observations,givingrecommendationsfordatarescue,qualitycontrol,timeseries homogenization,andotherdatamanagementissues,andgivingadviceontheuse ofobserveddataintheanalysisofclimatevariabilityandclimaticextremeevents.
Thisbookhasthreemaintopics:climateobservations,dataqualitycontrol,and timeserieshomogenization.Theintentionandpracticaleffortstoproducemore accuratedataandspatiallyandtemporallymorecorrectlycomparabledataisthe commonline,whichconnectstheseratherdifferenttopics.Thetimeserieshomogenization(referredbrieflytoashomogenization)takesthelargestplaceinthebook. Thedetailedpresentationofhomogenizationissueshasfourreasons:theirhigh potentialimpactontheaccuracyofclimatetrendestimations,thefastmethodologicaldevelopmentofhomogenizationinthemostrecentdecades,thescientific
complexityofthetopic,andthelackofalternativesourcesregardingthematically orderedandsufficientlydetailedscientificpresentationsofthistopic.
Thecompletepresentationofclimateobservationswouldhavealargerextent thantheentirebook,so,wefocusonthelandsurfaceobservationsandgivealess detailedpresentationoftheothersegmentsofclimateobservations.Thereason forthisdistinctionisthatlongtimeseriesofspatiallydenseandaccurateobservationsareaccessiblefirstforlandsurfaceobservations;hence,thehighestlevelof spatialandtemporalcomparabilitycangenerallybeachievedforsuchclimatic records.Notethatalthoughtheobservationsofchemicalcomponentsoftheatmospherearealsoclimateobservations,weskipthissubtopicduetothelargemethodologicaldifferencesincomparisonwithrecordingandanalyzingthemore traditionallyobservedclimaticelementsrelatingdirectlytoweathervariations.
Sincethelate1980sand1990s,boththescientificcommunityandthepublicare moreawareaboutthethreatsofacceleratingglobalwarming,andtheimportanceof possessingaccurateandcorrectlycomparableclimaticdataseemsobvious.However,theintentiontoimprovedataaccuracyasmuchaspossibleisfarolderthan therecognitionoftheseverityofglobalwarmingissues.Ittookmonthsforaclimate observeraspirantinthe20thcenturytolearnthecorrectperformanceofinstrumental andsubjectiveobservations.Anaspirantobserverwasregularlyaccompaniedbyan experiencedobserverwhoexplainedhundredsofrulesofthecorrectobservation practices.Certainrulessometimesseemedtoominute.However,thebestclimate observerswouldratherrecord100unimportantdetailsthantomissonly1ofthe importantones.Wewouldnothavelong,high-qualityclimatetimeserieswithout theirwork.Ibelievethatabookpresentingthebestpracticesofhigh-qualityclimate dataproductionismorethanasourceofinformation.Itisalsoahomagetotheparticipantsinvolvedintheproductionofhigh-qualityclimaticdatabases,namelyengineersandmanufacturersofprecisemeteorologicalinstruments;officialscharged withdatacollectionanddataarchiving;studentswhodigitizedahugeamountofdata tosavethemforfuturegenerations;upperairwindobserverswhofollowedvisually thetrajectoryofaballoon,keepingtheireyesgluedtotheopticalinstrument(theodolite)for30–60minunderdifferentweatherconditions;thebravemenwhotraveledtoicypolarregionstoperformclimateobservationsanddiscovernewdetailsof theEarth’sclimate;residentswhonevertraveledanywhere,astheyperformedclimateobservationsatthesameobservationsiteoneachdayoftheyearwithoutinterruptionsforholidays.
Thebookhas10chapters. Chapter1 presentsthelandsurfaceobservations.Both instrumentalandsubjectiveobservationsarediscussed,butthepresentationofthe instrumentalobservationsforsomekeyclimaticelements,i.e.,temperature,precipitationtotal,windspeed,winddirection,relativehumidity,atmosphericpressure, andsunshineduration,isdetailedmorethanthatoftheotherclimaticelements.From the1990s,thetraditionalmeteorologicalinstrumentshavebeenchangedtoautomaticweatherstations(AWS)inmostcountriesoftheworld,andwepresentboth thetraditionalandAWSinstrumentations.
In Chapter2,wedeviatefromthemaintopicofthebook,whichdealswithdata onlandsurfaceobservations,andpresenttheothersegmentsofclimateobservations. Inthischapter,theradiosondeobservationsarepresentedinmoredetail,astheyprovideinsighttothethree-dimensionalbehaviorofsomeessentialclimaticelements liketemperature,humidity,andairflowstructures.Thentheroleofmeteorological satellitesandradarsinclimateobservationsisdiscussed.Althoughtheseremote sensingobservationsaregenerallylessaccuratethanthelocalobservations,they areindispensableforclimatemonitoringinthelesspopulatedregionsoftheEarth, andalsoformonitoringsomespecialclimateproperties.
In Chapter3,themaintopicisthequalitycontroloftheobserveddata.The sourcesofdataerrors,theindicatorsthatsuggestthelikelyoccurrenceofdataerrors, andthedifferentqualitycontrolproceduresarediscussedwithseveralexamples.
From Chapter4toChapter9,theprincipaltopicisthetimeserieshomogenization.Homogenizationexaminespersistentbiases;thistopicismorecomplicatedthan thequalitycontrol,fortheaddedtimedimension.In Chapter4,thegeneralconcepts ofhomogenizationarepresented,andtheconnectionbetweenqualitycontroland homogenizationisdiscussed.
In Chapters5–7,therulesandoptionsforrelativehomogenizationarepresented. Thisisthemostimportantgroupofhomogenizationmethods.Inrelativehomogenization,persistentnon-climaticbiasesofacandidateseriesofobservedclimatic valuesaredetectedbyspatialcomparisonsbetweenthecandidateseriesandtime seriesofneighboringstations.Thereisalargevarietyofrelativehomogenization methods.Despitethescientificcomplexityofthetopic,themainrulesandconclusionsareeasytounderstand,whilethedetaileddescriptionsservepartlyasjustificationsandpartlytoofferauniqueintellectualadventureforinterestedreaders.
In Chapter8,themostfrequentlyusedhomogenizationmethodsarepresented.In addition,somemodernhomogenizationmethodsshowinghighaccuracyinmethod comparisontestsarealsopresented.
Chapter9 isdedicatedtothetopicofefficiencytestsofhomogenizationmethods. Theaccuracyofhomogenizationmethodscanbetestedonsyntheticallydeveloped testdatasets.Wediscusswithexampleswhysuchtestsareimportant,whichcharacteristicsmakeatestdatasetappropriatefortesting,andwhichfactorslimitthe potentialaccuracyofhomogenizationmethods.
Chapter10 presentsexampleswheretheaccessibilityandaccuracyofobserved climaticdataarehighlyimportant.Dataaccuracyisnotlessimportantfortheelaborationofclimatepredictionsandclimatechangescenariosthanforrevealing Earth’sclimateinthepast,andinthischapterweshowthereasonswhy.
ThebookissupplementedwithanAppendixthatdescribessomebasicstatistical conceptsandrelations.ReaderscanconsultthisAppendixatanytimeiftheyfeelthe necessitytorenewsuchknowledgewhenreadingthemainchaptersofthebook.
Themajorpartofthematerialinthisbookismyowncollection.ForthepresentationofAWSinstrumentationandupperairobservations,Ireceivedthematerial fromtwoofmyformercolleagues,Ro ´ bertTo ´ thandLa ´ szlo ´ Nyitrai,HungarianMeteorologicalService,researchfellowsintheareasoflandsurfaceobservationsand upperairobservations,respectively,andwewrotethesesectionstogether.
Landsurfaceobservations 1 Inthischapter,themaininstrumentsandmethodsoflandsurfaceobservationsare presented.Wegothroughtheactivitiesofsurfaceobservingstationsfocusingmost onclimaticelements,whichhavelongrecordsinnumerousobservingsites.These areinharmonywiththe essentialclimatevariables (ECV)determinedbytheGlobal ClimateObservingSystem(https://gcos.wmo.int)forthenearsurfacepartofthe atmosphere:temperature,watervapor,precipitation,windspeedanddirection, atmosphericpressure,andsurfaceradiationbudget(Bojinskietal.,2014).Both thetraditional,manualobservationsandthosewithautomatedinstrumentsarepresented.Inmostcasesofdetaileddescriptions,andfortheessentialclimatevariables always,thepresentedmethodologiescharacterizetheprofessionalobservations organizedbytheHungarianMeteorologicalService(HMS),asthereIwasobserver inthe1980s,andthusIhavepersonalexperiencesfromthatobservingnetwork.In Hungary,theregularinstrumentalclimateobservationsstartedin1781inBuda(part ofthelatercapitalBudapest),andthenationalmeteorologicalinstitute(laterHungarianMeteorologicalService)starteditsoperationasearlyasin1870.TheperformanceofHungarianclimateobservationshasbeenfollowinghighinternational standards;therefore,IbelievethatthepresentationoftheHungarianobservation practicesaddsaparticularvaluetothecontentofthischapter.
ReadersinterestedinawiderandmoregeneralpresentationofclimateobservationsmayconsulttherelevantandopenlyaccessibleWorldMeteorologicalOrganization(WMO)issue(WMO,2018a).
1.1 Globalsystemofweatherandclimateobservations Climaticelementscanbeobservedwithwatchingsubjectivelytheweatherprocesses orwithreadingmeteorologicalinstruments.Forinstance,cloudtypesorraindurationcanbeobserveddirectlybyeyes,whiletheobservationofatmosphericpressure orradiationtotalisunimaginablewithoutmeteorologicalinstruments.Formany otherclimaticelements,subjectiveobservationscanprovideroughestimationsonly: Anobservermayfeelthatthetemperatureishighorlow,mayseethetracesthatlotof rainhavebeenfallen,etc.,butsuchestimationsareinsufficientforthequantitative descriptionofweatherandclimate.Inclimateobservations,theuseofmeteorologicalinstrumentsisgeneralfromthe18thcenturyforobservingtemperature,precipitationamountandatmosphericpressure,althoughthenumberofobservingsiteswas
ClimateObservations. https://doi.org/10.1016/B978-0-323-90487-2.00011-6 Copyright # 2023RoyalMeteorologicalSociety.PublishedbyElsevierInc.incooperationwithTheRoyalMeteorologicalSociety. Allrightsreserved.
verysmallbefore1850.Fromthesecondhalfofthe19thcentury,observingnetworksbecamedenser,theinstrumentalobservationsoffurtherclimaticelements havebeenestablished,andtheunificationofobservationruleshasbegunwiththe foundationofnationalmeteorologicalinstitutesinmanydevelopedcountries.
Theclimaticrecord“surfaceairtemperature ¼ 20.0°C”referstoaphysicalstate oftheairnearthegroundsurface.Ideally,agivenrecordshouldmeanthesameatany placeoftheworld,andthemeaningshouldbeindependentfromthetimeofthe observation.Manyeffortshavebeendedicatedtounifyobservingrules,firstby thenationalmeteorologicalinstitutesandthenbytheWMO,butsomegeographical differenceshavebeenremainedfortraditions,politicalreasons,andalsoformaintaininglongseriesofobservationswithoutmethodologicalchanges.Anobstacleof unifyingobservingrulesinternationallywasandhasremainedthatwhilesuchunificationsfavorthegeographicalcomparability,mightdoharminthetemporalcomparabilityofclimaterecordsofanobservingsite.Furtherproblemisthenatural differencesofgroundsurface.Unifiedobservingrulescannotalwaysbeprovided forregionsofdeserts,forests,permafrostareas,etc.
Around1990thetransitiontotheuseofautomaticweatherstations(AWS) started,andthedevelopedcountriesfinishedthistransitioninorbeforethefirst decadeofthe21stcentury.Note,however,thatmanualobservationsarestillperformedforsomeclimaticelements.Inourreviewaboutclimateobservations,the instrumentationsofboththemanualweatherstations(MWS)andAWSarepresented,butwecannotpresentthediversityofinstrumentsappliedindifferentcountriesanddifferenteras.Welimitthepresentationofclimateobservationstosome typicalMWS(AWS)instrumentsusedinthe1980s(around2020)inacentralEuropeancountry,Hungary.Theseorverysimilarinstrumentswiththesameornearlythe sameinstallationsareappliedinmanyothercountriesoftheworld.
Thischapterisgenerallydedicatedtothepresentationofthelandsurfaceclimate observations,butbeforestartingthat,herewereviewbrieflythewholesystemof climateobservations.
• Landsurfaceobservations:Theseobservationsareperformedbyobserversorby AWSs.Theinstrumentsareusuallyplacedabout1–2mheightabovetheground surface.Oftenalargenumberofclimaticelementsareobservedinagiven observingsite,andtheclimaterecordscanbelongerthan100years.
• Marineclimateobservations:Marineobservationsareperformedinships,by usingbuoys,orwithremotesensingfrommeteorologicalsatellites.Though shipobservationshavelonghistory,wededicatelittlespacetomarine observationsfortworeasons:shipobservationsprovidedastronglyuneven spatialcoverageofmarineclimaterecords,andthespatialandtemporal comparabilityofdataisgenerallypoorerthanforlandsurfacedataforthe spatiallyandtemporallychangingconditionsofmarineobservations.Inthenext chapter,somesatellite-basedmarineobservationswillbebrieflydiscussed (Section2.7),whileforreadersinterestedinshipandbuoyobservationswe recommend WMO(2018b) and Hemsley(2015)
• Radiosondes:Aradiosondecomprisesanelectricthermometer,anelectric hygrometerandatransmitter.Afterthereleaseoftheradiosondefromitshost station,itelevatesuptoabout30kmheightin60–90minbyaballoonfilledwith hydrogenorhelium,anditmonitorsandtransmitsthephysicalpropertiesofthe atmospherearoundit.Radiosondesareusedfromaboutthemiddleofthe20th century,andthemostaccurateandspatio-temporalcoherentobservationsofthe troposphereandlowerstratosphereareprovidedbythem.
• Meteorologicalsatellites:Surfaceandupperairpropertiesaremonitoredfrom meteorologicalsatellitessincethe1960s.Sensorsofmeteorologicalsatellites interceptthenaturalelectromagneticradiationemittedfromtheearthsurfaceand atmosphere,andtheevaluationofintensitydistributionaccordingtoradiation wavelengthsprovidesthetransformationfromdetectedradiationtoobserved climaticvalues.Alargevarietyofclimatevariablesandclimateindicatorsurface propertiesareobservedbythemallovertheEarth.
• Meteorologicalradars:Similarlytosatellites,radarsaremodernremotesensing toolsinmeteorology.Theyemitelectromagneticwaves,whichreflectfrom raindrops,snowflakesandiceparticlesofclouds.Weatherradarsdetectthe development,positionandintensityofthunderstorms,hailstorms,theicing conditionsincloudyareas,andalsoareaaverageprecipitationamountscanbe measuredbythem.Upperairwindscanalsobeobservedbyradars.
Radiosonde,satelliteandradarobservationswillbepresentedin Chapter2.Turning backtothepresentationoflandsurfaceobservations,thefirstthingtobeenhancedis thatdifferentkindsofobservingstationsexistwithdifferentinstrumentationsand schedulesofobservations.AWSsallowcontinuousobservationofclimaticelements, butcontinuousobservationswereperformedalsointheMWSerain synopticstations.Oneimportantroleofsynopticstationswastoprovidecontinuousobservations andfrequentdatatransmissionsforweatherforecastsandweatheralarmsystems. TheobserverofanMWScodedthemajorityoftheactuallyobservedclimatecharacteristicsineveryhour,andemittedareporthavingcomprisedaseriesofdigits.
Apartoftheclimateobservationsareoftenorganizedoutofthenationalmeteorologicalinstitutes,asclimateobservationswithspecialobjectivesservehydrological,military,agronomicalpurposes,ormonitoringlocalclimatesincitiesorcoastal areas.Suchobservationprogramsandthedatamanagementmightbeorganized jointlywithmeteorologicalinstitutes,buttheseissuesvaryaccordingtocountries andoftenalsoaccordingtohistoricalperiods.Inseveralcountries,themeteorologicalandhydrologicalmanagementsorthecivilandmilitaryservicesareunifiedinstitutionally.However,theunificationofdataobtainedbydifferentkindsof observationmanagementsmightreducethespatialandtemporalcomparabilityof climaterecords,oratleasttheunificationneedsspecialattentioninthedatamanagementprocedures.
InHungary,24synopticstationsoperatedinthe1980swiththecontinuousobservationofmanyweatherandclimateelements,whiletheprogramoffurther60 principalclimatologicalstationswereusuallylimitedtotheobservationofprecipitation,
temperature,humidityandsignificantweathereventslikefog,thunderstorm,etc. Beyondtheinstitutionallyorganizednetwork,voluntaryobserversof precipitation observingstations contributedtothespatiallydenseobservationofprecipitation andsignificantweatherevents.WiththeinstallationofAWSs,thedivisiontosynopticstationsandprincipalclimatologicalstationsceased,butthespatialdensityof observationsstillhavedifferencesaccordingtoclimaticelements.Themainreason ofthesedifferencesisthedifferencesinthedegreeofthenaturalspatialvariability accordingtoclimaticelements.
In2020,125AWSswereoperatedbyHMS,andfurther142byGeneralDirectorateofWaterManagement(GDWM).InthemajorityoftheGDWMstations,only precipitation,snowcover,andsnowwatercontentmeasurementsareperformed, althoughinafewofthemseveralotherclimaticelementsarealsoobserved.There isaclosecollaborationbetweenHMSandGDWM,HMShelpsintheprofessional controlandmaintenanceoftheGDWMinstruments,andtheobserveddataof GDWMaresharedwithHMS.
ReferenceobservingstationofHungarianMeteorologicalServiceinBudapestPestszentlo ˝ rinc (2021).Theinstrumentnearesttothecameraisalightningdetector.
TheobserveddataofAWSsarecodedandtransmittedfromtheobservingsitesinto theHMScenterinevery10minbytheAWScomputersandaWebapplicationsysteminstalledinthecenter.ThefastandhighqualityfulfillmentofcodingandtransmissionisclearlymoreassuredwiththenewAWScomputersandmodern transmissionchannelsthanwithanyearliersystem.
Precipitationobservationsneedthehigheststationdensity,firstlyforitsoutstandingimportanceinhydrology,watermanagementandagriculture,andsecondlyfor thegenerallyhighspatialvariationofthefallenprecipitation.InHungary,thesystem ofvoluntaryobserverssatisfiesthisneed.Voluntaryobserversaretrainedcivil observersworkingforprecipitationobservationstations.Thenumberofthesestationsdecreasedsincethemiddleofthe20thcenturyfrom800to430.Around 2020,approximatelyhalfoftheprecipitationobservingstationshavealreadybeen automated,andtheirdatawerecollectedbytheHMSautomaticdatatransmission system.Fromthestilloperatingmanualprecipitationobservingstations,the observerssentdailyreportsoftheamountandformofthefallenprecipitation.Voluntaryobserversareencouragedtosendareportimmediatelywhenanextraordinary weathereventhasbeenoccurred(e.g.,heavyrain,hailstorm,etc.).Thedatasentfrom precipitationobservingstationsaresubjectedtoprofessionalqualitycontrolinthe samewayasanyotherobservedclimaticdata.
IntheobservingnetworkofHMS,thevisualobservationofcloudinessandsignificantweathereventshasnotcompletelyceased,butsuchobservationsservemore weatherforecaststhanclimatologicaluse.Around2020,14professionalobservers performedvisualobservations.
1.2 Siteselectionandinstallationofinstruments Observedclimaticcharacteristicsaregenerallyexpectedtoberepresentativeforthe regionoftheobservingsite.Therefore,asfarasitispossible,flatareaswithspatially uniformsurfaceuseandvegetationcoverareselectedtobeobservingsites.However,aswearealsointerestedinlearningtheclimateofcoastalormountainousareas, severalexceptionsoccurinpracticewherethedataofobservingsitescharacterize moretheclimateofspecificlocationsthantheregionalclimate.Thespatialrepresentativenessofobserveddatadependsalsoontheobservedclimaticelements. Forinstance,thespatialrepresentativenessisgenerallyhigh( 100km)overplanes fortemperature,irradiationandatmosphericpressurewhenlocalweatherphenomenalikeshowers,fogs,etc.donotdisturbthespatialuniformityofweathercharacteristics,whilethespatialvariabilityisthehighestforcloudinessandprecipitation amount.
Professionalmeteorologicalinstruments,eitherofMWSorAWS,areinstalledin anenclosedareaof“instrumentland,”farfromanylocalinfluence(e.g.,highbuildings,smokesource,roadswithtraffic).Therecommendedsizeofinstrumentlandis atleast25m2 25m2,butnotethatthesizemaydependfromthedistancefrom potentiallydisturbingnearbyobjects,observationprogramofthestation,and 5 1.2
sometimesalsofrompracticallimits.Thesurfaceoftheinstrumentlandiscovered byshortgrasswherethesoilandclimateallowgrasslands,orremainsbareinthe reversecase.
Inselectingtheplacesofindividualinstruments,distancekeepingfromthefence oftheinstrumentlandandpossibleothernearbyobjectsmustbeconsidered.Radiometersarethemostdemandinginstrumentsregardingtheirlocations,asanylimitationoftheincomingradiationbytheshadowofsurroundingobjectswould directlyaffecttheobservedradiation.Thelocationofwindmeasuringinstruments alsoneedsdistinguishedattention,andforthemtheheightaboveothernearby objectshaskeyimportance.Windspeedandwinddirectionareaffectedbythelocal geographicalunevennessofthesurfaceandanynaturalorhumanmadeobjectsbeing inthewayofairstreams.Therefore,itisrecommendedtoplacethewindmeasuring instrumentsatleastafewmetersabovethetopofallnearbyobjects.Thermometers andhygrometersalsocanbeaffectedbylocaldistortionsofairstreamsortheradiationofnearbyobjects,buttheimpactsofthesefactorsaregenerallylowinanappropriatelyselectedinstrumentland.Moreimportantly,thermometersandhygrometers mustbeplacedtoaclearlyhigherlevelthanthesurfaceofplantswithintheinstrumentland,andtheymustbeprotectedagainstdirectradiationeffectsandthedirect effectsofweatherphenomenalikerain,snow,icedeposition,etc.
Summarizing,thefollowingfactorsinfluencethespatialrepresentativenessofthe observedclimaticdata:
•Geographicalcharacteristicsofthesite,likeexposure,distancefromwaterbodies anddistancefromthenearesthighobjects;
•Preparationandmaintenanceofinstrumentland;
•Locationofmeteorologicalinstrumentswithintheinstrumentland;
•Heightofmeteorologicalinstrumentabovethesurfaceandaboveothernearby objects;
•Protectionofcertaininstrumentsfromdirectweathereffects.
1.3 Manualandautomatedobservations Untilthe1990s,relativelyfewAWSswereinstalledintheworld,mainlyinplaces hardlyaccessibleforlocalinhabitants(Hartletal.,2020).Inotherplaces,theinclusionofhumanobserversfacilitatedahigherqualityandcompletenessofobservations,andMWSsweremoreeconomicthanAWSs.Withthedevelopmentof morepreciseandmoreeconomicautomatictoolsthissituationwasgraduallychanged,andAWSsbecamethebestandmosteconomicobservationtoolsaround1990. InHungary,theautomatizationofsynopticandprincipalclimatologicalstationswas completedbetween1990and2000.Inmanysynopticstations,bothAWSandMWS observationswereperformedduringsomeyearsofthetransitiontoAWSmode,and theseparallelmeasurementshelptoeliminateinhomogeneitiesfromthetimeseries ofclimaterecords.
MannedweatherstationinnorthwesternHungary(Sopron)accordingtoanoldphotograph. Intheright,thereisaStevensonscreen,inwhichthethermometersandhygrometerswere placed.Bottomintherightatraditionalprecipitationgaugestands.Ontheleftsidecablesare exposedtoobservepossibleicedepositionsfromair(hoar,rimeoricingrain).Notethelarge distancesbetweenthestationbuildingandthemeteorologicalinstruments.
Traditionalprecipitationgaugesarestillinuseinseveralprecipitationobservingstations.Thewithdrawalofobserverstafffromsynopticstationsandprincipalclimatologicalstationswasgradualandcompletedabout2015leavingonlyone professionalobservertoobservethecloudinessandweatherphenomenaover 7–10sitesofthewesternpartofcountrybywebcameras,aswellasoneobserver intheeasternpartofthecountryfor7sites.Whensignificantweatherphenomena (e.g.,fog,thunderstorm,snowfall)areexpected,allthe14officialobserversare instructedtocarryoutlocalvisualobservations.Beyondtheprincipalchangethat thecontributionofhumanobserverswasminimized,someotherimportantchanges arerelatedtotheautomatizationofobservations:(i)Newinstrumentswithnewsensorsareused;(ii)Mechanicalrecordinginstrumentsarenolongerused;(iii)Accuracyofobservedphysicalquantitieshasgenerallybeenimproved;(iv)Apartofthe visualobservationshavebeenmechanized,whileanotherpartofthemaresimplified orevenabandoned;(v)Timingsofobservationshavebecomeaccurate;(vi)In recordingdailytemperaturemaximumsandminimumsAWSsconsiderthe24h periodofcalendardays;(vii)Calculationsandcodinghavebeencomputerized;(viii) Datarecordinganddatatransmissiontothehostinstitutehavebeenmodernized.All thesechangesmayinfluencethetemporalcomparabilityofclimaterecords;therefore,wediscussthemmore.
(i) Newinstrumentswithnewsensorsareused.Thenewsensorsareoftenelectric tools,i.e.,theiroperationiseitherrelatedtothechangesofsomeelectric propertiesasaresponsetothechangesofmeteorologicalconditions,ortothe emission/absorptionofelectromagneticwaves.Thenewsensorsaregenerally moreaccurate,althoughsomeexceptionsoccur.Thenewsensorsareusually
smallerandcharacterizedbyshorterresponsetimethanthesensorsofthe MWSinstruments.Thischangeisgenerallyfavorable,butmightaffectthe temporalcomparabilitybetweenoldandnewobservations.NotethatAWSs mayincludeinstrumentswithnonelectricsensors,andinthiscase,the instrumentissuppliedwithatransducertoprovidedigitalrecording.
(ii) Mechanicalrecordinginstrumentsarenolongerused.IntheMWSera,the continuousrecordingofsomeclimateelementswassolvedbymechanical recordinginstruments.Theyoperatedwithpensandinkdrawinggraphsona chart.Thechartwasfittedoverthesurfaceofaclock-drivenrevolvingdrum.
Amechanicalbarographfrom1930(Wikimedia).
Thepensmovedverticallyinfunctionofthetransmittedsignsofthesensors, whilethedrumandthechartmovedhorizontallyaroundtheverticalaxisof thedrum.Thedrummadeawholecirclewithin1dayor1week,hencedaily orweeklychartswereproduced.Inmostcases,theseinstrumentswerelessaccuratethanthebaseinstrumentsofthestation,duetotheerrorsinmechanicaltransmissiontothepensandthefrictionbetweenthepenandthepaperofthechart.The recordsoftheseinstrumentsservedtocontrolthecorrectoperationofthebase instrumentsandtoprovidedetailsaboutthetemporalchangesoftheobserved climatevariable,buttheydidnotsubstitutethereadingsofthebaseinstruments inpredeterminedtimings.WiththetransitiontoAWSs,thecontinuousrecording ofclimateobservationsissolvedwithmoremodernandaccuratetools.
(iii) Theaccuracyofobservedphysicalquantitiescharacterizingweatherand climatehasgenerallybeenimproved.Thisimprovementhasthreesources: TheAWSinstrumentsareusuallymoreaccuratethanMWSinstruments(with someexceptions);observers’errorsareexcluded;datatransformationanddata transmissionerrorsarepracticallyexcluded.
(iv) Apartofthevisualobservationshasbeenmechanized,whileanotherpartof themissimplifiedorevenabandoned.Someclimatecharacteristicslikeair transparencyandcloud-baseheightareobservedmoreobjectively,withnewly developedinstruments,whileseveralkindsofobservationslikecloudtypes, fogpatchesoropticalphenomenaaresimplifiedorabandoned.Notethattime
seriesofthelattertypesofclimatevariableshaverarelybeenexamineddueto thediscretedatastructure.
(v) Timingsofobservationshavebecomeaccurate.InAWSs,therecordingof observedclimateiscontinuous,whileintheMWSeratheinstrumentswereread andvisualobservationswereperformedaccordingtoadefinedtimeschedule. Thescheduleofsynopticstationsprescribedthemostabundantobservation programjustbeforetheso-calledmainterminuses,i.e.,before00,06,12,and18 UTC.Themostintenseobservationprogramwasbefore06UTC.Thecoded reportmusthavebeenready10–15minbeforetheterminushavingbeenthe nominaltimeofobservations.Theobservationswereusuallyperformed10–30minbeforetheterminus,andthistimelapsewasusuallysufficienttoperform thenecessarycalculationsandcodingtotheterminusreport.However,severe weathereventssometimesmadetheobservers’workmuchmoredifficult.For instance,thesnowprecipitationmusthavebeenmeltedbeforethemeasurement ofitswatercontentandthisneededtime,causing60–100mintimelapsesinthe recordingofheavysnowevents.Incaseofharshweatherconditions,the observercouldhavebeenoverloadedwithsimultaneoustasksalsoforthe difficultaccess(snowy-icypaths)tothemeteorologicalinstrumentswhich couldhavealsobeenaffectedbysnow-icedeposition.Allsuchproblemsand errorsourceshavebeendisappearedwiththeintroductionofappropriately designedAWSs.WhendataofMWSandAWSobservationsarecompared,the averagetimelapseofMWSobservationsmustbetakenintoaccount.
(vi) Inrecordingdailytemperaturemaximumsandminimums,AWSsconsiderthe 24hperiodofcalendardays.Nothingseemstobemorenormalthantobasea dailyvaluetotheperiodbetween00and24hofthatday.However,sincemost climatologicalstationswereoperatedwithoutobservationsduringnights, thermometerswerereadat06UTCand18UTC,butnotat00UTC.Therefore, thetechnologicalchangecausedacertainincompatibilitybetweenthe recordeddailytemperatureminimums/maximumsofMWSeraandthoseof theAWSobservations(Holderetal.,2006).
(vii) Calculationsandcodinghavebeencomputerized,andwiththis,anerror sourcehasbeeneliminated.
(viii) Datarecordinganddatatransmissiontothehostinstitutehavebeen modernized.IntheMWSera,firsteverythingwasrecordedinpapers.Reports ofsynopticstationsweretransmittedbyaradiotelephonesystem,while extraordinaryweathereventswerereportedbyradiogramsfromanykindof observingstations.Reportsofsynopticstationswerecollectedandrecorded manuallyatthattime.IntheAWSera,theclimateobservationsarerecorded ondiscs,andthereportstothehostinstitutesaredeliveredviaInternet.
1.4 Temperature Airtemperatureisobservedwiththermometerssincethe18thcentury.Itwasknown fromtheearliestperiodsofobservationsthatthermometersmustbeshelteredfrom thedirectsunshinetoobtainspatio-temporalcomparabledata.However,early
shelteringmethodswereoftenmarkedlydifferentfromthemodernstandards,e.g.,in theverybeginningsathermometerwassometimesputnearthenorthernwallofa buildingtoimpedetheeffectsofdirectsunshine.Suchaninstallationdidnotprotect againstweatherphenomenaotherthansunshine,inadditionanearbywallmayactas apositiveornegativeheatsourceimpactingthermometerobservations.
Theremightarisethequestionwhysunshineandotherweathereffectsmustbe impededduringtemperatureobservations,asinnaturalconditionsoftennothing impedessuchweathereffects.Infact,insunnydaysathermometerexposedtodirect sunshineoftenshowsvaluesclosertothehumanthermalcomfortwefeelthantothe regularlymeasuredtemperatures.Itmustbeclarifiedthattheaimoftemperature observationsistomeasurethetemperatureoftheair,whichgenerallydiffersfrom thetemperatureofanythermometerwhenitisexposedtosunshineorotherweather effects.Itisbecausethethermometerasasolidobjectreactsinadifferentwaytothe impactsofweathereffectsthanthegasesoftheatmosphere.Giventhattheweather effectsonthermometersdependonvariousfactors(size,materialandcolorofthermometeretc.),accuratetemperatureobservationsneedtheexclusionofdirectradiationandweathereffects.
Variousshelteringmethodswereappliedinthehistoryoftemperatureobservations.TheuseofStevensonscreenswasstartedinthesecondhalfofthe19thcentury, andthisshelteringmethodbecamedominantinthefirstdecadesofthe20thcentury. TheStevensonscreenisawhite,louveredbox,whichallowsairflowstogothrough byslits,butfullyexcludesradiationsfromanyobjectoutside,andeffectivelyprotectsagainstprecipitationparticles.TheexternalsurfaceofStevensonscreensis whitetominimizetheradiationabsorptionofthescreen,whichotherwisecouldelevatethetemperatureoftheinteriorofthescreenabovethetemperatureoutside.With thetransitiontoAWS,theuseofStevensonscreensceased,firstlybecausetheAWS sensorsaremuchsmaller,hencetheydonotneedsobigscreens.AWSthermometers andhygrometersareshelteredbyasmallshieldabovetheinstruments.
InaMWSsynopticstationofHMS(hereafter:inaMWS)severalthermometers wereinuse,servingdifferentpurposes.Thermometerseventookpartinairhumidity measurements(Section1.5).Inthemajorpartofthe20thcentury,mercuryinglass tubethermometerswerethemostwidelyusedinstrumentsofprofessionaltemperatureobservationsinallovertheworld.Mercurychangesitsvolumelinearlywith temperature,andresistswellagainstotherphysicalorchemicalalterations.However,fromthebeginningofthe21stcentury,theuseofmercuryhasbeenceased inallareasforitsseveretoxicityanddangertohealthandthenaturalenvironment.
TheprincipalthermometerofaMWSwasamercurythermometer,andwasread ineveryhour.Itstankwasat2mheightabovethegroundsurface(1.25minsome countries),andwasreferredtoasstationthermometer.Thesethermometerscouldbe readwith0.1°Cprecision,andtheywereveryreliableandaccurateinstruments. However,theyhadthedrawbackthattheycouldnotfollowcorrectlyfasttemperaturechangesduetotheirrelativelylargesizeandtheshelteringbyStevensonscreen.
Forclimatestudies, dailyminimumtemperatures(Tmin)and dailymaximum temperatures(Tmax)areoftenthemostimportantpiecesoftemperaturerecords. ItisbecausetheTminandTmaxseriesaregenerallyavailablefrommorestations
andforlongerperiodsthanothertemperatureobservations. Dailymeantemperature canbeapproximatedbythearithmeticalmeanofTminandTmax,orfromthereadingsofthestationthermometer.Thelatteristheoreticallymoreprecise,butitcanbe appliedonlywhenthehoursofthermometerreadingsareconstantinthestation.
InMWSs,TminandTmaxaremeasuredwithspecificinstruments.Minimum thermometersincludealcoholandafinesticklyingintheglasstube.Itisplaced inhorizontalpositionintheStevensonscreen,at2mheightabovethegroundsurface.Whentemperaturedecreases,thevolumeofthealcoholcontracts,andthealcoholdragsthestickdowninawaythattheupperendofthestickshowstheactual temperature.Whentemperatureincreases,thealcoholexpands,butitleavesthestick atthepositionofthelowesttemperatureoccurred.AfterreadingTmin,thestickpositionisfittedtotheactualtemperaturebyturningthethermometerinverticalposition forafewseconds.Classicmaximumthermometersincludedmercurywhosetop levelshowedthemaximaltemperaturesinceitslastreading,aslongastheupperpart ofthemercuryhasnotbeenshakendownmanuallytotheleveloftheactualtemperature.Inthe20thcentury,thesekindsofthermometerswerewidelyusedbyhealth servicesandindividualstocontrolfever.Minimumthermometersandmaximum thermometerswerereadat06UTCand18UTC,andtherecordedvaluesreferring totheprevious12hperiod.
Beyondthethermometersdescribedinthepreviousparagraphs,athermograph servedtoproduceweeklychartsoftheobservedtemperatures.Itssensorwasa bimetalwhosechangewithtemperaturemovedthepenupordown,viamechanical transmissionunits.
InAWSs,PT100resistancethermometersareused.Theirsensorisafineplatinumfilmclosedintoaprotectortube.Theelectricresistanceofplatinumincreases quadraticallywithgrowingtemperature,andtheparametersofthephysicalrelationshipareknownwithhighprecisenessfromlaboratorymeasurements.Thisthermometerissmall,facilitatinganotablylowerresponsetimethanMWSthermometers. Theinstrumentconvertsautomaticallytheobservedresistancetotemperature. ThenominalaccuracyofPT100thermometersis 0.3°C.Thethermometer,like theothersensorsofanAWS,isconnectedtoadatalogger,withwhichtherecording ofobserveddataispracticallycontinuous,sothatnoalternativethermometersare neededtomeasureTminandTmax.
MeanvaluesoftemperaturerecordsoriginatedfromMWSorAWSobservations aregenerallycomparablewithoutnotablesystematicbias,butitislesstrueforTmin andTmaxrecords.Especially,thelowerresponsetimeofAWSthermometersallows todetectthepeaktemperaturesofshort-termthermalfluctuations.Thismightpush upwardtherecordedTmaxvalues,althoughnotethattheAWSrecordedTminand Tmaxvaluesare1-minaverages.
1.5 Humidity Airhumidityisanimportantmeteorologicalelement.Itinfluencesthespeedofmoisturelossofsoilandplantsandtheperceivedthermalcomfortofhumansandanimals.
